On the saltation of fresh snow in a wind tunnel: Profile characterization and single particle statistics
Bibliographic record
Abstract
We present experimental results on the snow drift in a turbulent boundary layer over a flat fresh snow‐covered surface. Vertical profiles of mass flux and of the distribution of particle diameters were obtained by means of a pair of Snow Particle Counters parallel with measurements of the stream‐wise velocity profile. The aim of the paper is to discuss current parameterizations of the vertical mass flux profile for fresh snow and to investigate the range of timescales involved in a developing saltation layer occurring in a turbulent boundary layer. The novelty of the work consists of using an intact fresh snow cover as an erodible surface able to provide realistic snow crystals as drifting particles. Results show that (1) the parameters scaling the vertical mass flux profiles of fresh snow can significantly differ from those given in the literature for ice or compacted snow particles; (2) though drifting snow covers an extremely wide range of temporal scales, the mean time interval between saltating particles 〈Δ t 〉 is the key timescale of the saltation process; (3) 〈Δ t 〉 allows for the optimal reconstruction of the mass flux as a continuous signal and for neglecting the effects related to the heterogeneous distribution of particle size on the mass flux. Implications on the modeling of snow drift and on the processing of field observations are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".